System and method for dynamically updated unmanned vehicle navigation planning

ABSTRACT

A system and method for dynamically updated vehicle navigation planning for a second UV based on navigation feedback of a first UV. The first UV navigates based on a first navigation plan and the second UV navigates based on a second navigation plan. The system includes: a processing circuitry; and a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to: receive the navigation feedback of the first UV, wherein the navigation feedback of the first UV includes telemetry data indicating at least a divergence event and a location of the divergence event, wherein the divergence event is a divergence of the first UV from the first navigation plan; and dynamically update the second navigation plan based on the navigation feedback of the first UV when the second navigation plan includes the location of the divergence event.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/361,505 filed on Jul. 13, 2016, the contents of which are herebyincorporated by reference.

TECHNICAL FIELD

The present disclosure relates generally to unmanned vehicles, and moreparticularly to updating navigation plans for multiple unmannedvehicles.

BACKGROUND

Unmanned vehicles (UVs) are seeing increased industry use asimprovements in fields such as artificial intelligence, battery life,and computation are made. UVs may be used for purposes such asphotography and delivery. As an example, companies such as Amazon® areincreasingly using UVs such as drones to deliver packages. As a result,some companies will likely begin to utilize hundreds or thousands of UVsat once to provide services.

Control over UVs may be complicated, due in part to a need to balanceautonomous control with manual control. One particular use for UVs iscontrolling a fleet of UVs simultaneously, where control becomesexponentially more complicated. Manual control of each and every UV maybe undesirable due to, e.g., excessive labor costs, human error, and thelike.

As a result of the rapid adoption of UVs, regulators are scrambling toadapt to technological breakthroughs, with safe navigation becoming anincreasingly important issue. Navigation of civilian UVs over longdistances, especially without line of sight, makes it difficult forpilots to optimize their trajectory due to hazards and other in-flightevents. These challenges and others result in less efficient navigation.These inefficiencies may be minor with respect to a single UV's flightpath, but can result in significant accumulated inefficiencies whenmultiple UVs are used.

It would therefore be advantageous to provide a solution that wouldovercome the deficiencies of the prior art.

SUMMARY

A summary of several example embodiments of the disclosure follows. Thissummary is provided for the convenience of the reader to provide a basicunderstanding of such embodiments and does not wholly define the breadthof the disclosure. This summary is not an extensive overview of allcontemplated embodiments, and is intended to neither identify key orcritical elements of all embodiments nor to delineate the scope of anyor all aspects. Its sole purpose is to present some concepts of one ormore embodiments in a simplified form as a prelude to the more detaileddescription that is presented later. For convenience, the term “someembodiments” may be used herein to refer to a single embodiment ormultiple embodiments of the disclosure.

Certain embodiments disclosed herein include a system for dynamicallyupdated unmanned vehicle navigation planning for a second UV based onnavigation feedback of a first UV. The first UV navigates based on afirst navigation plan and the second UV navigates based on a secondnavigation plan. The system comprises: a processing circuitry; and amemory, the memory containing instructions that, when executed by theprocessing circuitry, configure the system to: receive the navigationfeedback of the first UV, wherein the navigation feedback of the firstUV includes telemetry data indicating at least a divergence event and alocation of the divergence event, wherein the divergence event is adivergence of the first UV from the first navigation plan; anddynamically update the second navigation plan based on the navigationfeedback of the first UV when the second navigation plan includes thelocation of the divergence event.

Certain embodiments disclosed herein also include a non-transitorycomputer readable medium having stored thereon instructions for causinga processing circuitry to perform a process, the process comprising:constantly receiving, in real-time, navigation feedback of a firstunmanned vehicle (UV), wherein the first UV navigates based on a firstnavigation plan, wherein the navigation feedback of the first UVincludes telemetry data indicating at least a divergence event and alocation of the divergence event, wherein the divergence event is adivergence of the first UV from the first navigation plan; anddynamically updating a second navigation plan based on the navigationfeedback captured by the first UV when the second navigation planincludes navigating through the location of the divergence event,wherein a second UV navigates based on the second navigation plan.

Certain embodiments disclosed herein also include a method fordynamically updated unmanned vehicle navigation planning for a second UVbased on navigation feedback of a first UV. The first UV navigates basedon a first navigation plan and the second UV navigates based on a secondnavigation plan. The method comprises: receiving the navigation feedbackof the first UV, wherein the navigation feedback of the first UVincludes telemetry data indicating at least a divergence event and alocation of the divergence event, wherein the divergence event is adivergence of the first UV from the first navigation plan; anddynamically update the second navigation plan based on the navigationfeedback of the first UV when the second navigation plan includes thelocation of the divergence event.

Certain embodiments disclosed herein also include a system fordynamically updated unmanned vehicle navigation planning for a second UVbased on navigation feedback of a first UV. The first UV navigates basedon a first navigation plan and the second UV navigates based on a secondnavigation plan. The system comprises: a processing circuitry; and amemory, the memory containing instructions that, when executed by theprocessing circuitry, configure the system to: constantly receive, inreal-time, the navigation feedback of the first UV, wherein thenavigation feedback of the first UV includes telemetry data indicatingat least a divergence event and a location of the divergence event,wherein the divergence event is a divergence of the first UV from thefirst navigation plan; and dynamically update the second navigation planbased on the navigation feedback captured by the first UV when thesecond navigation plan does not include navigating through the locationof the divergence event, wherein the updated second navigation planincludes navigating through the location of the divergence event.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter disclosed herein is particularly pointed out anddistinctly claimed in the claims at the conclusion of the specification.The foregoing and other objects, features, and advantages of thedisclosed embodiments will be apparent from the following detaileddescription taken in conjunction with the accompanying drawings.

FIG. 1 is a schematic diagram of a navigation planning system accordingto an embodiment.

FIG. 2A is a schematic diagram of an unmanned aerial vehicle.

FIG. 2B is a schematic diagram of an unmanned aerial vehicle and apayload.

FIG. 3A is an illustration of an unmanned aerial vehicle following anavigation plan

FIG. 3B is an illustration of an unmanned aerial vehicle receiving andexecuting an updated navigation plan.

FIG. 4 is a flowchart illustrating a method for dynamically updatedunmanned vehicle navigation planning according to an embodiment.

DETAILED DESCRIPTION

It is important to note that the embodiments disclosed herein are onlyexamples of the many advantageous uses of the innovative teachingsherein. In general, statements made in the specification of the presentapplication do not necessarily limit any of the various claimedembodiments. Moreover, some statements may apply to some inventivefeatures but not to others. In general, unless otherwise indicated,singular elements may be in plural and vice versa with no loss ofgenerality. In the drawings, like numerals refer to like parts throughseveral views.

The various disclosed embodiments include a method and system fordynamically updated unmanned vehicle (UV) navigation planning. A firstnavigation plan is generated for a first UV. Navigation feedback isreceived from the first UV. The navigation feedback may includetelemetry data indicating a divergence from the first navigation plan, adivergence location at which the first UV diverged from the firstnavigation plan, a timestamp, and the like. A second navigation plan isgenerated for a second UV. Based on the navigation feedback of the firstUV, the second navigation plan is dynamically updated. The updatedsecond navigation plan may include navigating around the divergenceevent location or otherwise avoiding obstacles at the divergence eventlocation, or may include navigating to the divergence event location inorder to utilize the divergence event for more efficient navigation.

It should be noted that the embodiments disclosed herein allow for moreefficient navigation by multiple UVs than, for example, staticallysetting a navigation path for each UV prior to navigation. Inparticular, the disclosed embodiments may be utilized to update UVnavigation plans in real-time so as to allow UVs to avoid obstaclesencountered by other UVs. Moreover, some embodiments disclosed hereinmay result in further increased efficiency of navigation by updatingnavigation plans in real-time to benefit from environmental conditions.For example, a navigation plan for a second UV navigating East may bedynamically updated to include moving to a location in which a first UVencountered wind blowing East, thereby propelling the second UV Eastusing the Eastward-blowing wind.

FIG. 1 shows an example schematic diagram of a navigation planningsystem 100 according to an embodiment. The navigation planning system100 includes a processing circuitry 110, a memory 120, a storage 130,and a communication interface 140. In an embodiment, the components ofthe navigation planning system 100 may be communicatively connected viaa bus 105.

The processing circuitry 110 may be realized as one or more hardwarelogic components and circuits. For example, and without limitation,illustrative types of hardware logic components that can be used includefield programmable gate arrays (FPGAs), application-specific integratedcircuits (ASICs), Application-specific standard products (ASSPs),system-on-a-chip systems (SOCs), general-purpose microprocessors,microcontrollers, digital signal processors (DSPs), and the like, or anyother hardware logic components that can perform calculations or othermanipulations of information.

The memory 120 may be volatile (e.g., RAM, etc.), non-volatile (e.g.,ROM, flash memory, etc.), or a combination thereof. The memory 120 mayfurther be used as a working scratch pad for the processing circuitry110, as a temporary storage, and the like. Computer readableinstructions to implement one or more embodiments disclosed herein maybe stored in the storage 130.

The memory 120 may include a first memory portion 122 for storingsoftware. Software shall be construed broadly to mean any type ofinstructions, whether referred to as software, firmware, middleware,microcode, hardware description language, or otherwise.

Instructions may include code (e.g., in source code format, binary codeformat, executable code format, or any other suitable format of code).The instructions, when executed by the processing circuitry, cause theprocessing circuitry 110 to perform the various processes describedherein. Specifically, the instructions, when executed, cause theprocessing circuitry 110 to at least dynamically coordinate navigationplans for multiple UVs.

In yet another embodiment, the memory 120 includes a second memoryportion 124 having stored thereon navigation feedback received from atleast one UV. The navigation feedback may include telemetry data relatedto navigation by the UV. Specifically, the navigation data may include,but is not limited to, divergences from navigation paths, locations ofdivergences, times, and the like.

The storage 130 may be magnetic storage, optical storage, and the like,and may be realized, for example, as flash memory or other memorytechnology, CD-ROM, Digital Versatile Disks (DVDs), or any other mediumwhich can be used to store the desired information. The storage 130 maystore instructions for causing processing circuitries to execute themethods described herein, and the like. In some embodiments, the storage130 further includes a storage portion 135. The storage portion 135 maystore therein navigation plans for one or more UVs.

The communication interface 140 allows the navigation planning system100 to communicate with, for example, UVs, or a combination thereof, forpurposes such as sending navigation plans and receiving telemetry data.The network interface 140 may be a wireless connection, for example overa network.

It should be understood that the embodiments described herein are notlimited to the specific architecture illustrated in FIG. 1, and otherarchitectures may be equally used without departing from the scope ofthe disclosure.

FIG. 2A is an example schematic diagram of an unmanned aerial vehicle(UAV) 200 that may be provided UV navigation plans in accordance withvarious disclosed embodiments. In an example implementation, the UAV 200receives navigation plans from and sends telemetry data to thenavigation planning system 100, FIG. 1.

The UAV 200 includes a body 210 coupled to a first rotor 222, a secondrotor 224, a third rotor 226, and a fourth rotor 228. The UAV mayfurther include a first landing skid 232 and a second landing skid 234.

The body 210 houses a controller 215 configured to control locomotion ofthe UAV 200 based on one or more navigation plans. The controller 215may be coupled to a communication circuit (not shown) for communicatingwith a control server such as the navigation planning system 100. Thecommunication circuit allows for communications such as, but not limitedto, sending telemetry data, receiving navigation plans, and the like.

In the example diagram shown in FIG. 2A, the body further includessensors 217. The sensors 217 may include, but are not limited to, acamera, a microphone, a global positioning system (GPS), anaccelerometer, a gyroscope, a magnetometer, a proximity sensor, ananemometer, a wind vane, a light sensor, a barometer, a thermometer, aradiation sensor, and the like. The sensors 217 may be utilized tocollect telemetry data related to navigation by the UAV 200 such as, forexample, geographical location, direction, acceleration, orientation,speed, wind speed, wind direction, and the like.

In an example implementation, a first pair of rotors (e.g., the firstrotor 222 and the third rotor 226) rotate in clockwise, and a secondpair of rotors (e.g., the second rotor 224 and the fourth rotor 228)rotate counterclockwise. The rotors may have a fixed position. Height,pitch, yaw, and roll of the UAV may be adjusted by applying a thrust toeach rotor.

The landing skids 232 and 234 may be equipped with dampers 236. Thedampers 236 assist in shock absorption from landing the UAV 200, therebyallowing for protection of at least a payload (e.g., the payload 240,FIG. 2B) and the controller 215.

FIG. 2B is an example schematic diagram of the UAV 200 equipped with apayload 240. In the example schematic diagram of FIG. 2B, the payload240 is affixed to a bottom portion of the UAV 200.

It should be noted that the UAV 200 is merely an example UV and that UVswhose navigation may be dynamically coordinated according to thedisclosed embodiments are not limited to the specific architectureillustrated in FIGS. 2A and 2B. Specifically, different numbers ofsensors, rotors, landing skids, or other components may be equallyutilized.

It should be further noted that navigation of UVs other than UAVs may beequally coordinated without departing from the scope of the disclosure.Even further, navigation may be coordinated between a UAV and anothertype of UV without departing from the scope of the disclosure. Forexample, navigation plans for a UAV may be updated based on telemetrydata indicating strong wind that is received from a ground-based UV(e.g., an autonomous car). Example UVs may include unmanned or uncrewedvehicles such as, but not limited to, an unmanned ground vehicle (e.g.,an autonomous car), an unmanned aerial vehicle or unmanned combat aerialvehicle (e.g., a drone), an unmanned surface vehicle, an autonomousunderwater vehicle or unmanned undersea vehicle, an unmanned spacecraft,and the like.

FIG. 3A is an illustration 300A of the UAV 200 following a navigationplan. The illustration 300A includes the UAV 200 moving along a flighttrajectory 310 indicated in a navigation plan (e.g., a navigation planreceived from the Navigation planning system, not shown). At a location“A” on the flight trajectory 310, the UAV 200 encounters a strong gustof wind 305.

In some circumstances, the UAV 200 may apply additional thrust in orderto counter the gust of wind 305 and, thus, prevent divergence from thetrajectory 310. However, this may consume more power than navigating analternative flight trajectory 320 and returning to a point B on thetrajectory 310. During navigation, the UAV 200 is configured to send, tothe navigation planning system 100, telemetry data related to themovement along the trajectory 310 or 320. The telemetry data may beutilized to generate updated navigation plans for other UVs.

As a non-limiting example, the telemetry data may include geographiclocations of points A and B (i.e., the points at which the UAV 200diverged from and converged on the trajectory 310, respectively),geographic locations indicating the divergence from the trajectory 310(e.g., geographic locations of points along the trajectory 320) times(e.g., the times at which the UAV 200 reached point A and point B), winddirection of the gust of wind 305, wind speed of the gust of wind 305,and the like. The telemetry data values may be determined based on, forexample, the additional amount of force required to be applied by theUAV 200 to counter the gust of wind 305. The geographic location ofpoint A (e.g., the location of divergence) may be utilized to generatean updated navigation plan for another UV (e.g., the second UAV 350 asshown in FIG. 3B), thereby allowing the other UV to avoid the stronggust of wind 305. Alternatively, the geographic location of point A andthe direction of the strong gust of wind may be utilized to generate anupdated navigation plan for the other UV traveling in the same directionas the gust of wind 305, thereby allowing the other UV to navigate topoint A and benefit from the gust of wind 305 (i.e., the gust of wind305 blows in the same direction as the other UV is traveling, therebyaccelerating the other UV).

FIG. 3B is an illustration 300B of a second UAV 350 receiving andexecuting an updated navigation plan. The second UAV 350 is configuredto at least receive navigation plans from the navigation planning system100 (not shown in FIG. 3B). Specifically, in an example, the second UAV350 receives a second navigation plan including navigation throughpoints “A,” “C,” and “D.”

Upon receiving telemetry data related to the divergence of the first UAV200 from the trajectory 310, the Navigation planning system 200 isconfigured to generate an updated navigation plan for the second UAV350. The updated navigation plan includes navigation along a trajectory340 and through points “A,” “E,” and “D.” Rather than diverging from anoriginal navigation plan including movement along trajectories 310 and330, the UAV 350 moving according to the updated navigation plan mayutilize the gust of wind 305 to a navigational advantage by allowing theforce of the wind to, for example, replace a portion of the requiredthrust, thereby more efficiently navigating along the trajectory 340.The updated navigation plan may include merging the trajectory 340 withthe trajectory 330 of the original navigation plan for the UAV 350 atlocation “D.”

It should be noted that FIGS. 3A and 3B are illustrated and described asincluding navigation by the UAV 200 based on navigation plans generatedby the navigation planning system 100 merely for simplicity purposes,and that the embodiments disclosed herein are not limited to theparticular architectures of the UAV 200 and the navigation planningsystem 100.

FIG. 4 is an example flowchart 400 illustrating a method for dynamicallycoordinated UV navigation planning according to an embodiment. In anembodiment, the method is performed by the navigation planning system100, FIG. 1. In an example implementation, the method may be utilized togenerate an updated navigation plan for the second UAV 350 based onnavigation feedback received from the first UAV 200.

At S410, a navigation plan is generated for the first UAV 200. Thenavigation plan for the first UAV 200 may be generated based on, forexample, a starting location and an ending location for navigation bythe first UAV 200.

At S420, navigation feedback is received from the first UAV 200. Thenavigation feedback includes telemetry data captured by sensors of thefirst UAV 200 such as, but not limited to, geographical location,direction, acceleration, orientation, speed, wind speed, wind direction,and the like. The telemetry data may indicate, e.g., a divergence fromthe navigation plan for the first UAV 200, a location of the divergence,a time of the divergence, and the like. In an embodiment, S420 mayfurther include receiving meteorological data.

The navigation feedback may be received in real-time during navigationfrom the first UAV 200. Further, the navigation feedback may be receivedconstantly, i.e., repeatedly at predetermined time intervals. Receivingthe navigation feedback constantly in real-time allows for identifyingdivergence events that are currently occurring or have recentlyoccurred, thereby allowing for more accurate updating of the navigationplan for the second UAV 350, particularly with respect to temporarydivergence events that may occur at a location only for short periods oftime (e.g., passing thunderstorms or brief gusts of wind).Alternatively, the navigation feedback may be received subsequent tonavigation by the first UAV 200 (e.g., when the first UAV 200 returns toa home location).

In an embodiment, S420 may include determining a type of divergenceevent that caused the divergence based on the navigation feedback. Thetype of the divergence event may be, but is not limited to, a temporaryevent or a permanent event. A temporary event may be a single occurrenceevent such as passing of a flock of birds (i.e., an event that does notaffect other UVs and should not be used to generate updated navigationplans), or a non-single occurrence event such as wind or other weatherconditions that may last for hours or days. A permanent event may be,but is not limited to, a non-moving obstacle such as a building or anyother event that may affect navigation which does not change frequently(e.g., every few days). For example, a permanent event may occur when aheight of a building has changed such that all subsequently generatednavigation plans should account for the change.

Permanent events, single occurrence temporary events, and non-singleoccurrence temporary events may be defined with respect to telemetrydata. Each definition of an event may include, for example, a temporalthreshold and a counter. Which events to be utilized for generatingupdated navigation plans for the second UAV 350 may be determined basedon the telemetry data received from the first UAV 200 as compared to thetemporal threshold, the counter, or both.

In some implementations, the type of the divergence event is determinedbased on navigation feedback received from multiple UVs, aircrafts(e.g., planes, helicopters, etc.), or both. Determining the type ofdivergence event based on navigation feedback from multiple vehiclesallows for more accurate determination of divergence events. Forexample, a divergence event indicated by motion sensor data may be apassing object (e.g., a bird) or may be a static object (e.g., abuilding). When the divergence event is a bird at a location, navigationfeedback from multiple UVs navigating through that location willtypically only indicate one instance of the divergence event at thelocation. When the divergence event is a building at a location,navigation feedback from multiple UVs navigating through the locationwill indicate multiple instances of the divergence event.

The types of divergence events may be determined further with respect toan area in which the divergence occurred. Each area includes multiplelocations. Determining types of divergence events with respect to areasmay allow for, e.g., updating navigation plans based on navigationfeedback from multiple UVs. For example, based on navigation feedbackindicating a lightning storm from one UV at a first location andnavigation feedback that does not indicate a divergence event fromanother UV at a second location, an updated navigation plan for avoidingthe first location by navigating through the second location may begenerated.

In some embodiments, the cause of the divergence may be determined via amachine learning model using telemetry data from the first UAV 200 asinputs and providing event predictions as outputs. The machine learningmodel may be trained based on training telemetry data previouslyreceived from UVs, and may be further based on known training event dataassociated with the training telemetry data.

At S430, an original navigation plan is generated for the second UAV350. The navigation plan for the second UAV 350 may be generated basedon, for example, a starting location and an ending location fornavigation by the second UAV 350.

At S440, based on the navigation feedback from the first UAV 200, anupdated navigation plan may be generated, in real-time, for the secondUAV 350. In an embodiment, S440 may include determining, based on thenavigation feedback, a divergence event which caused the first UAV 200to diverge from a trajectory of the navigation path generated at S410.The divergence event is associated with a location (i.e., as indicatedby the telemetry data of the navigation feedback) such that the updatednavigation plan may include either avoiding the divergence eventlocation or navigating through the divergence event location. Thenavigation plan may be generated when the original navigation plan forthe second UAV 350 includes navigating through the divergence eventlocation and the divergence event is to be avoided by the second UAV350. Alternatively, the generated when the original navigation plan forthe second UAV 350 does not include navigating through the divergenceevent location and the divergence event is to be encountered by thesecond UAV 350 (e.g., when it is desirable to encounter the divergenceevent to, e.g., accelerate the second UAV 350).

The navigation feedback may be analyzed constantly (i.e., repeatedly atpredetermined time intervals) to determine divergence events, therebyallowing for dynamic updating of navigation plans for the second UAV 350based on divergence events occurring in real-time. The dynamic updatingallows for increasing efficiency of navigation with respect to, forexample, avoiding obstacles, utilizing existing sources for acceleration(e.g., wind), and the like.

In an embodiment, S440 may further include determining a type ofdivergence event and updating the navigation plan based on thedetermined type. The type of the divergence event may be a permanentevent, a single occurrence temporary event, or a non-single occurrencetemporary event, as described further herein above. The type ofdivergence event may further be a particular type of divergence eventsuch as, for example, obstacles, weather conditions, construction, andthe like. As a first example, the updated navigation plan may not bedifferent from the original navigation plan when the divergence event isa flock of birds passing. As a second example, the updated navigationplan may be different from the original navigation plan when a currenttime is 3:00 PM PT and the divergence event is a storm occurring at 2:00PM PST and estimated to continue until 5:00 PM PST. As a second example,the updated navigation plan may be different from the originalnavigation plan when the divergence event is a change in height of abuilding.

At S450, the updated navigation plan is sent, in real-time, to thesecond UAV 350 for implementation, thereby dynamically configuring thesecond UAV 350 to more efficiently navigate with respect to thedivergence event.

It should be noted that the steps of the flowchart 400 are illustratedin the particular order shown in FIG. 4 merely for simplicity purposesand without limitation on the disclosed embodiments. Some of the stepsmay be performed in a different order or in parallel without departingfrom the scope of the disclosure. As a particular example, thenavigation plans for the first UAV 200 and for the second UAV 350 may begenerated in parallel, or the original navigation plan for the secondUAV 350 (i.e., the plan generated at S430) may be generated prior to thenavigation plan for the first UAV 200 and updated after the first UAV200 begins moving in accordance with the navigation plan generated atS410. Further, the original navigation plan for the second UAV 350 maybe generated based on the navigation feedback from the first UAV 200such that the original navigation plan may include navigating so as toavoid or utilize an obstacle as described herein.

It should be further noted that the embodiments described herein abovewith respect to FIG. 4 are discussed with respect to updating navigationof the second UAV 350 based on navigation feedback received from thefirst UAV 200 merely for simplicity purposes and without limitations onthe disclosed embodiments. Any UV configured to collect and sendtelemetry data may be utilized to generate updated navigation plans forany other UV configured to receive and implement navigation planswithout departing from the scope of the disclosure. Further, navigationfeedback from multiple first UVs may be utilized to update navigationplans for a second UV. Also, navigation feedback from a first UV may beutilized to update navigation plans for multiple second UVs.

It should be understood that various embodiments described herein aboveare discussed with respect to unmanned vehicles (UVs) and unmannedaerial vehicles (UAVs) merely for simplicity purposes and withoutlimitation on the disclosed embodiments. Manned vehicles, robots, or anyother system capable of controlled propulsion may be equally utilizedwithout departing from the scope of the disclosure such that thesolutions may provide navigation instructions for at least partiallyautonomous control of such other systems.

It should be understood that any reference to an element herein using adesignation such as “first,” “second,” and so forth does not generallylimit the quantity or order of those elements. Rather, thesedesignations are generally used herein as a convenient method ofdistinguishing between two or more elements or instances of an element.Thus, a reference to first and second elements does not mean that onlytwo elements may be employed there or that the first element mustprecede the second element in some manner. Also, unless stated otherwisea set of elements comprises one or more elements.

As used herein, the phrase “at least one of” followed by a listing ofitems means that any of the listed items can be utilized individually,or any combination of two or more of the listed items can be utilized.For example, if a system is described as including “at least one of A,B, and C,” the system can include A alone; B alone; C alone; A and B incombination; B and C in combination; A and C in combination; or A, B,and C in combination.

The various embodiments disclosed herein can be implemented as hardware,firmware, software, or any combination thereof. Moreover, the softwareis preferably implemented as an application program tangibly embodied ona program storage unit or computer readable medium consisting of parts,or of certain devices and/or a combination of devices. The applicationprogram may be uploaded to, and executed by, a machine comprising anysuitable architecture. Preferably, the machine is implemented on acomputer platform having hardware such as one or more central processingunits (“CPUs”), a memory, and input/output interfaces. The computerplatform may also include an operating system and microinstruction code.The various processes and functions described herein may be either partof the microinstruction code or part of the application program, or anycombination thereof, which may be executed by a CPU, whether or not sucha computer or processor is explicitly shown. In addition, various otherperipheral units may be connected to the computer platform such as anadditional data storage unit and a printing unit. Furthermore, anon-transitory computer readable medium is any computer readable mediumexcept for a transitory propagating signal.

All examples and conditional language recited herein are intended forpedagogical purposes to aid the reader in understanding the principlesof the disclosed embodiment and the concepts contributed by the inventorto furthering the art, and are to be construed as being withoutlimitation to such specifically recited examples and conditions.Moreover, all statements herein reciting principles, aspects, andembodiments of the disclosed embodiments, as well as specific examplesthereof, are intended to encompass both structural and functionalequivalents thereof. Additionally, it is intended that such equivalentsinclude both currently known equivalents as well as equivalentsdeveloped in the future, i.e., any elements developed that perform thesame function, regardless of structure.

What is claimed is:
 1. A system for dynamically updated navigationplanning for a second unmanned vehicle (UV) based on navigation feedbackof a plurality of first UVs, wherein each of the plurality of first UVsnavigates based on a respective first navigation plan, wherein thesecond UV navigates based on a second navigation plan, comprising: aprocessing circuitry; and a memory, the memory containing instructionsthat, when executed by the processing circuitry, configure the systemto: receive the navigation feedback of the plurality of first UVs,wherein the navigation feedback includes telemetry data indicating atleast a divergence event and a location of the divergence event, whereinthe divergence event is a divergence of at least one of the plurality offirst UVs from its respective first navigation plan; determine a type ofthe divergence event based on the navigation feedback of the pluralityof first UVs, wherein the type of the divergence event is based furtheron a number of instances of the divergence event; dynamically update thesecond navigation plan based on the navigation feedback and the type ofdivergence event when the second navigation plan includes the locationof the divergence event; and send the updated second navigation plan tothe second UV.
 2. The system of claim 1, wherein the type of thedivergence event is any one of: a permanent event, and a temporaryevent.
 3. The system of claim 1, wherein the type of the divergenceevent is determined using a divergence event type model, wherein thedivergence event type model is created based on machine learning usingtelemetry data of a plurality of fourth UVs as inputs.
 4. The system ofclaim 1, wherein the updated second navigation plan includes navigatingto avoid the location of the divergence event.
 5. The system of claim 1,wherein portions of the navigation feedback of the first UV areconstantly received in real-time.
 6. A non-transitory computer readablemedium having stored thereon instructions for causing a processingcircuitry to perform a process, the process comprising: constantlyreceiving, in real-time, navigation feedback of a plurality of firstunmanned vehicles (UVs), wherein each of the plurality of first UVsnavigates based on a respective first navigation plan, wherein thenavigation feedback of the first UV includes telemetry data indicatingat least a divergence event and a location of the divergence event,wherein the divergence event is a divergence of at least one of theplurality of first UVs from the respective first navigation plan;determining a type of the divergence event based on the navigationfeedback of the plurality of first UVs, wherein the type of thedivergence event is based further on a number of instances of thedivergence event; dynamically updating a second navigation plan based onthe navigation feedback and the type of divergence event when the secondnavigation plan includes navigating through the location of thedivergence event, wherein a second UV navigates based on the secondnavigation plan; and sending the updated second navigation plan to thesecond UV.
 7. A method for dynamically updated navigation planning for asecond unmanned vehicle (UV) based on navigation feedback of a pluralityof first UVs, wherein each of the plurality of first UVs navigates basedon a respective first navigation plan, wherein the second UV navigatesbased on a second navigation plan, comprising: constantly receiving, inreal-time, the navigation feedback of plurality of first UVs, whereinthe navigation feedback includes telemetry data indicating at least adivergence event and a location of the divergence event, wherein thedivergence event is a divergence of at least one of the plurality offirst UVs from its respective first navigation plan; determining a typeof the divergence event based on the navigation feedback of theplurality of first UVs, wherein the type of the divergence event isbased further on a number of instances of the divergence event;dynamically updating the second navigation plan based on the navigationfeedback and the type of divergence event when the second navigationplan includes navigating through the location of the divergence event;and sending the updated second navigation plan to the second UV.
 8. Themethod of claim 7, wherein the type of the divergence event is any oneof: a permanent event, and a temporary event.
 9. The method of claim 7,wherein the type of the divergence event is determined using adivergence event type model, wherein the divergence event type model iscreated based on machine learning using telemetry data of a plurality offourth UVs as inputs.
 10. The method of claim 7, wherein the updatedsecond navigation plan includes navigating to avoid the location of thedivergence event.
 11. A system for dynamically updated navigationplanning for a second unmanned vehicle (UV) based on navigation feedbackof a plurality of first UVs, wherein each of the plurality of first UVsnavigates based on a first navigation plan, wherein the second UVnavigates based on a second navigation plan, comprising: a processingcircuitry; and a memory, the memory containing instructions that, whenexecuted by the processing circuitry, configure the system to:constantly receive, in real-time, the navigation feedback of theplurality of first UVs, wherein the navigation feedback includestelemetry data indicating at least a divergence event and a location ofthe divergence event, wherein the divergence event is a divergence of atleast one of the plurality of first UVs from the respective firstnavigation plan; determining a type of the divergence event based on thenavigation feedback of the plurality of first UVs, wherein the type ofthe divergence event is based further on a number of instances of thedivergence event; dynamically update the second navigation plan based onthe navigation feedback and the type of divergence event when the secondnavigation plan does not include navigating through the location of thedivergence event, wherein the updated second navigation plan includesnavigating through the location of the divergence event; and send theupdated second navigation plan to the second UV.
 12. The system of claim11, wherein the divergence event is wind blowing in a direction, whereinthe updated second navigation plan includes navigating through thelocation of the divergence event in the direction of the wind.